MP48-03 CONTRAST ENHANCED ULTRASOUND WITH PARAMETRIC MAPS FOR THE DETECTION OF PROSTATE CANCER

MP48-03 CONTRAST ENHANCED ULTRASOUND WITH PARAMETRIC MAPS FOR THE DETECTION OF PROSTATE CANCER

THE JOURNAL OF UROLOGYâ Vol. 193, No. 4S, Supplement, Sunday, May 17, 2015 e595 MP48-03 MP48-04 CONTRAST ENHANCED ULTRASOUND WITH PARAMETRIC MAPS...

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THE JOURNAL OF UROLOGYâ

Vol. 193, No. 4S, Supplement, Sunday, May 17, 2015

e595

MP48-03

MP48-04

CONTRAST ENHANCED ULTRASOUND WITH PARAMETRIC MAPS FOR THE DETECTION OF PROSTATE CANCER

HOW RELIABLE IS A NEGATIVE MRI/TRUS FUSION BIOPSY? THE NEGATIVE PREDICTIVE VALUE OF TARGETED BIOPSY FOR PROSTATE CANCER

Arnoud Postema*, Amsterdam, Netherlands; Peter Frinking, Plan-lesOuates, Switzerland; Martijn Smeenge, Theo De Reijke, Jean De la Rosette, Amsterdam, Netherlands; Francois Tranquart, Plan-lesOuates, Switzerland; Hessel Wijkstra, Amsterdam, Netherlands INTRODUCTION AND OBJECTIVES: Reliable imaging is needed to improve prostate cancer(PCa) diagnostic pathways. The altered microvascularity of malignant tissue is targeted by dynamic contrast enhanced ultrasound (DCE-US). Parametric maps generated by software that extracts perfusion parameters from DCE-US recordings can aid interpretation, increasing accuracy and decreasing user-dependency. This study aims to investigate the value of DCE-US, the added value of parametric maps and their potential to reduce the number of negative biopsy cores. METHODS: For 651 biopsy locations in 82 consecutive patients that underwent DCE-US imaging, we correlated DCE-US interpretation with and without parametric maps with biopsy results. We used SonoVueâ (Bracco, Milan, Italy) as contrast agent and parametric imaging software developed by Bracco Suisse SA (Geneva, Switzerland). We performed a stringent analysis including all positive cores and a clinical analysis including only cores with 10% of Gleason 7. We determined the potential reduction in biopsies (negative on imaging) and resulting missed PCa (false negatives). We calculated sensitivity, specificty, Positive Predictive Value (PPV) and Negative Predictive Value (NPV) on the per-patient level. RESULTS: DCE-US alone classified 470/651 (72.2%) of the biopsies as benign. In the stringent analysis 71 (15.1%) of these were false negatives and in the clinical analysis 40(8.5%). Including parametric maps 411/651 (63.1%) biopsies were classified as benign. Assessed stringently 50 (12.1%) were false negative, clinically 23 (5.6%) were false negative. The clinical per-patient analysis produced our most interesting results: DCE-US classified 36/82 patients as not needing biopsies, missing 8 diagnoses. Including parametric maps, 29/82 patients were classified as benign resulting in 3 missed diagnoses. Sensitivity, specificty, PPV and NPV were 73%, 58%, 50% and 79% for DCE-US alone and 91% (p¼0.0588), 56%, 57% and 90% with parametric maps. The figure shows a parametric map with the red zone indicating a high chance of PCa. The corresponding biopsy showed Gleason 3þ4 PCa. CONCLUSIONS: In our study of 651 biopsy locations a good prediction of biopsy outcome could be made using DCE-US with parametric maps. In our data almost two-thirds of the biopsy cores could be omitted with a modest decrease in PCa detection.

Source of Funding: This project was financially supported by the Dutch Cancer Society

Rachael Sussman*, Washington, DC; Michele Fascelli, Thomas Frye, Arvin George, Steven Abboud, Raju Chelluri, Richard Ho, Anna Brown, Sandeep Sankineni, Maria Merino, Ismail Turkbey, Peter Choyke, Bradford Wood, Peter Pinto, Bethesda, MD INTRODUCTION AND OBJECTIVES: Multiparametric magnetic resonance imaging (mpMRI) has been shown to improve clinically significant prostate cancer (CaP) detection. Targeted biopsy using MRI/ transrectal ultrasonography (TRUS) fusion is emerging as a novel diagnostic tool. The negative predictive value (NPV) of an MRI/TRUS fusion targeted biopsy of a suspicious lesion on mpMRI was determined. METHODS: 30 of 181 men who underwent radical prostatectomy from 2008-2014 were retrospectively identified and had at least one lesion on mpMRI that was negative for cancer on MRI/TRUS fusion biopsy. Whole mount pathology specimens, considered the gold standard for CaP detection, were aligned with MRI to assess true histopathology of all identified target lesions. Lesions negative for CaP on biopsy and not identified as cancer on pathology were considered true negatives (TN). Lesions biopsied negative but later found to possess foci of CaP on whole mount were considered false negatives (FN). Calculations of NPV were then made per biopsy year, per MRI suspicion score, and per lesion size on MRI. RESULTS: 48 lesions of a total 81 identified on mpMRI were reported negative for CaP in the 30 patients who underwent MRI/TRUS fusion biopsy. Of these, 37 lesions were found to be truly negative on histopathology, while 11 lesions had foci on CaP on the whole mount pathologic specimen. Overall NPV was 77% (37/48). NPV calculations were made per biopsy year, per MRI suspicion score, and per lesion size on MRI (Table 1). The NPV increased over time, and was as high as 85.7% in the most recent years. CONCLUSIONS: This series demonstrated a NPV of 77% for targeted MRI/TRUS fusion biopsy of lesions seen on mpMRI. The increasing NPV trend noted over time may have further applications to assess the learning curve for this diagnostic method. Not surprisingly, NPV is higher for low and moderately suspicious lesions than for highly suspicious lesions. This data may help urologists interpret the clinical significance and implications of a negative fusion biopsy.

NPV

True Negative Lesions

False Negative Lesions

Total Negative Lesions

2008-2009

76.9

10

3

13

2010-2011

64.3

9

5

14

2012-2014

85.7

18

3

21

Total

77.1

37

11

48

MRI Suspicion Score

NPV

True Negative Lesions

False Negative Lesions

Total Negative Lesions

Low

75.0

15

5

20

Moderate

80.8

21

5

26

High

50.0

1

1

2

Size on MRI (cm)

NPV

True Negative Lesions

False Negative Lesions

Total Negative Lesions

 0.5

70.0

7

3

10

> 0.5 & < 1.0

78.6

11

3

14

 1.0 & < 1.5

84.6

11

2

13

 1.5

72.7

8

3

11

Year

Source of Funding: National Institute of Health - Intramural Research Funding